Decoupling Adaptive Smith Prediction Model of Flatness Closed-Loop Control and Its Application

Song, Mingming; Liu, Hong‐Min; Xu, Yuzhe; Wang, Dongcheng; Huang, Yunpeng

doi:10.3390/pr8080895

Cited by 5 publications

(2 citation statements)

References 23 publications

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“…Therefore, the focus should be shifted to the control of secondary asymmetry. In addition, a coupling effect between the amounts of IRB and the IRS exists in the strip shape control mechanism of rolling mills [22]. Consequently, the coupling relationship between the impact of varying IRS amounts on asymmetric IRB and that of different IRB on asymmetric IRS should be analysed.…”

Section: Combined Effect Of Asymmetrical Irb and Irsmentioning

confidence: 99%

Asymmetric Shape Control Ability and Mutual Influence of the S6-High Cold Rolling Mill

Yuan,

Sun,

Guo

et al. 2024

Metals

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The control of the asymmetric shape of strips has always been an important and difficult part of the production of cold rolling strips. In this paper, the S6-High cold rolling mill is taken as the research object. A finite element model of this mill is constructed using ABAQUS 2022 software, and a multistage working condition simulation analysis is carried out. The independent effects of asymmetric Intermediate Roll Bending (IRB) and asymmetric Intermediate Roll Shifting (IRS) on the strip shape are investigated by constructing an asymmetric convexity evaluation index. The equivalent relationship between the asymmetric roll bending and the asymmetric roll shifting was determined by analysing the coupling effect of the benchmark bending and shifting rollers on their asymmetric shape control characteristics. The on-site application shows that optimizing the amount of preset asymmetric shape control can significantly improve the asymmetric situation of the shape, providing theoretical guidance for the asymmetric shape control of the S6-High cold rolling mill.

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Section: Combined Effect Of Asymmetrical Irb and Irsmentioning

confidence: 99%

Asymmetric Shape Control Ability and Mutual Influence of the S6-High Cold Rolling Mill

Yuan,

Sun,

Guo

et al. 2024

Metals

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“…Flatness [4][5] refers to the degree of buckling of the plate belt, including the dimension indexes of longitudinal and transverse dimensions of the plate belt. Transverse aspect refers to the section flatness (thickness distribution in the width direction of the plate), that is, the convexity of the plate.…”

Section: Introductionmentioning

confidence: 99%

Flatness Prediction of Cold Rolled Strip Based on EM-TELM

2021

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Flatness of cold rolled strip is an extremely important indicator of quality, and flatness control is the key technology of the modern high-accuracy rolling mill. The establishment of an efficient and accurate flatness prediction model is conducive to improving the flatness accuracy and realizing the effective control of flatness. Inspired by the error minimization principle, error minimized extreme learning machine with two hidden layers (EM-TELM) used to automatically determine the optimum hidden nodes is proposed in the paper, which is applied to establish the flatness prediction model of cold rolled strip. EM-TELM uses the block matrices to solve the output matrix of the second hidden layer. EM-TELM randomly adds one or a group of hidden nodes to the current network every time. During the increasing process of the network structure, the weights matrix connecting the hidden layer and the output layer are updated incrementally. Since EM-TELM is a no analytic method, it can be used in a kind of prediction problem for complex and difficult modeling systems. The experimental results indicated that the accuracy of EM-TELM is higher than that of EM-ELM, and EM-TELM reduces the computational complexity and training time compared to TELM which recalculates the parameters between different hidden layers when the network structure changes. INDEX TERMS Block matrices, cold rolled strip, error minimization, extreme learning machine, flatness prediction, two-hidden-layer.

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